Image sensor based terminals are known to be used in industrial data collection applications. For example, image sensor based indicia reading terminals have been used for a number of years for purposes of decoding information encoded in bar code symbols. Commercially available image sensor based terminals have monochrome image sensors that are preferred for their high signal to noise ratios that facilitate reliable decoding of bar code symbols by processing a captured image through one or more decoding algorithms.
In some applications, users take pictures with image sensor based terminals. However, the monochrome images produced using these image sensor based terminals can be of poor visual quality, with the resulting image data being stored in large files that can only be processed using proprietary hardware or software. Some applications require further image processing to correct distortions and enhance overall image quality. In other applications, using shade quantization to reduce the number of shades represented in an image may necessary. This image processing can involve converting and manipulating the image data in binary form for convenience, efficiency and storage considerations.
Because of these distortions and imperfections, in cases where the original subject matter of the picture is binary in nature, for example a single-color document, barcode or fingerprint, a binary representation may not provide a true representation of the original image, and more than two shades may be necessary to accurately represent it. In other cases, where the original subject matter or image inherently contains more than two shades, for example a form having a gray watermark or a map with various colored regions, generally a binary image cannot be used to represent the original and additional shades may be necessary. In these situations, it is often necessary to use more than one bit per pixel to represent an image.
It would be useful to have a system and method for storing and retrieving monochromatic images in binary format, whereby each pixel in the image is represented by a single binary value, while still providing a good quality representation of the original image, thereby reducing storage space requirements and facilitating processing of that image by legacy systems designed to process binary files.